EB-GLS: an improved guided local search based on the big valley structure

Shi, Jialong; Zhang, Qingfu; Tsang, Edward

doi:10.1007/s12293-017-0242-5

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…Guided local search is a metaheuristic optimization method that tops the list of local search algorithms by applying a penaltybased strategy that helps it escape local optima to find a better solution. The core concept is to keep adding penalties to the goal function, which guides the search far from the locally optimal solution 24 . Every time the search algorithm finds the local 5/14 optimal, the GLS penalizes certain selected features of such a candidate solution.…”

Section: Guided Local Searchmentioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Preprint

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The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we propose a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work (PoUW) mechanism, in which miners use computing resources to optimize the travelling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when travelling between different locations. This strategy aims to take advantage of the miners’ efforts to minimize the distance travelled by salesmen by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used technique works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025.

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Section: Guided Local Searchmentioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Guided Local Search (GLS) is a powerful metaheuristic optimization method, recognized for its ability to escape local optima and find better solutions by using a penalty-based strategy 19 . In the context of finding the best path for a set of locations, GLS is utilized to improve the solution by penalizing certain features (edges) and adjusting the cost function iteratively.…”

Section: System Modelmentioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners’ efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works.

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“…Whenever GLS is trapped in a local optimum, edges from the local optimum are evaluated and penalized so that the local optimum is no longer locally optimal in the next round of local search. Shi et al [22] improved GLS based on the big valley assumption of the TSP. In the improved GLS, the algorithm maintains an elite solution and the edges in the elite solution will be protected from the penalization.…”

Section: Related Workmentioning

confidence: 99%

Homotopic Convex Transformation: A New Landscape Smoothing Method for the Traveling Salesman Problem

Shi

Sun

Zhang

et al. 2022

IEEE Trans. Cybern.

Self Cite

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This paper proposes a novel landscape smoothing method for the symmetric Traveling Salesman Problem (TSP). We first define the homotopic convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP. We observe that controlled by the coefficient of the convex combination, (i) the landscape of the HC transformed TSP is smoothed in terms that its number of local optima is reduced compared to the original TSP; (ii) the fitness distance correlation of the HC transformed TSP is increased. We then propose an iterative algorithmic framework in which the proposed HC transformation is combined with a heuristic TSP solver. It works as an escaping scheme from local optima for improving the global search ability of the combined heuristic. A case study with the 3-Opt local search as the heuristic solver shows that the resultant algorithm significantly outperforms iterated local search and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances.

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EB-GLS: an improved guided local search based on the big valley structure

Cited by 16 publications

References 31 publications

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Homotopic Convex Transformation: A New Landscape Smoothing Method for the Traveling Salesman Problem

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